[1]} introduced the idea of StyleGAN. In StyleGAN and its successor StyleGAN2, the generator can be considered split into a style network and a synthesis network. As with a traditional GAN, the style network takes an input \(\mathbf {z}\)  drawn from a distribution \(P_z\) . The network then maps \(z\)  to another higher dimensional space \(W\) , which are called "styles". This higher dimensional latent code is then input to the synthesis network to generate the image \(I\) . Due to the mixing of different styles during training and path length regularisation, the \(W\) -space latent codes of StyleGAN able to achieve better native disentanglement compared to a regular GAN.
